Comparison of Linear and Non-linear 2D+T Registration Methods for DE-MRI Cardiac Perfusion Studies

  • Gert WollnyEmail author
  • María-Jesus Ledesma-Carbayo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8896)


A series of motion compensation algorithms is run on the challenge data including methods that optimize only a linear transformation, or a non-linear transformation, or both – first a linear and then a non-linear transformation. Methods that optimize a linear transformation run an initial segmentation of the area of interest around the left myocardium by means of an independent component analysis (ICA) (ICA-*). Methods that optimize non-linear transformations may run directly on the full images, or after linear registration. Non-linear motion compensation approaches applied include one method that only registers pairs of images in temporal succession (SERIAL), one method that registers all image to one common reference (AllToOne), one method that was designed to exploit quasi-periodicity in free breathing acquired image data and was adapted to also be usable to image data acquired with initial breath-hold (QUASI-P), a method that uses ICA to identify the motion and eliminate it (ICA-SP), and a method that relies on the estimation of a pseudo ground truth (PG) to guide the motion compensation.


Independent Component Analysis Motion Compensation Free Breathing Synthetic Reference Linear Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Wollny, G., Hublin, J., Ledesma-Carbayo, M.J., Skinner, M., Kellman, P., Hierl, T.: MIA - A Free and Open Source Software for Gray Scale Medical Image Analysis. Source Code for Biology and Medicine 8:20 (2013)Google Scholar
  2. 2.
    Wollny, G., Kellman, P., Santos, A., Ledesma-Carbayo, M.J.: Automatic Motion Compensation of Free Breathing acquired Myocardial Perfusion Data by Using Independent Component Analysis. Medical Image Analysis 16(5), 1015–1028 (2012)CrossRefGoogle Scholar
  3. 3.
    Wollny, G., Ledesma-Carbayo, M.J., Kellman, P., Santos, A.: Exploiting Quasiperiodicity in Motion Correction of Free-Breathing Myocardial Perfusion MRI. IEEE Trans. Med. Imag. 29(8), 1516–1527 (2010)CrossRefGoogle Scholar
  4. 4.
    Li, C., Sun, Y.: Nonrigid registration of myocardial perfusion MRI using pseudo ground truth. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 165–172. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Kybic, J., Unser, M.: Fast Parametric Elastic Image Registration. IEEE Trans. Image Process. 12(11), 1427–1442 (2003)CrossRefGoogle Scholar
  6. 6.
    Rohlfing, T., Maurer Jr., C.R., Bluemke, D.A., Jacobs, M.A.: Volume-Preserving Nonrigid Registration of MR Breast Images Using Free-form Deformation with an Incompressibility Constraint. IEEE Trans. Med. Imag. 22, 730–741 (2003)CrossRefGoogle Scholar
  7. 7.
    Wollny, G., Kellman, P.: Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms. GigaScience 3:23 (2014)Google Scholar
  8. 8.
    Gupta, V., Hendriks, E., Milles, J., Van Der Geest, R., Jerosch-Herold, M., Reiber, J., Lelieveldt, B.: Fully Automatic Registration and Segmentation of First-pass Myocardial Perfusion MR Image Sequences. Academic Radiology 17(11), 1375–1385 (2010)CrossRefGoogle Scholar
  9. 9.
    Wollny, G., Ledesma-Carbayo, M.J., Kellman, P., Santos, A.: A new similarity measure for non-rigid breathing motion compensation of myocardial perfusion MRI. In: Proc. of the 30th Int. Conf. of the IEEE-EMBS, Vancouver, BC, Canada, pp. 3389–3392 (2008)Google Scholar
  10. 10.
    Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley (2000)Google Scholar
  11. 11.
    Nelder, J., Mead, R.: A Simplex Method for Function Minimization. Computer Journal 7, 308–313 (1965)CrossRefzbMATHGoogle Scholar
  12. 12.
    Vlcek, J., Luksan, L.: Shifted limited-memory variable metric methods for large-scale unconstrained minimization. J. Computational Appl. Math. 186, 365–390 (2006)CrossRefMathSciNetzbMATHGoogle Scholar
  13. 13.
    Wollny, G., Kellman, P.: Supporting material for: Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms. GigaScience, Database (2014).

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.BIT ETSI TelecomunicaciónUniversidad Politécnica de MadridMadridSpain
  2. 2.Ciber BBNZaragozaSpain

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